A fast algorithm is developed to implement a Bayesian beam-former that can estimate signals of unknown direction of arrival (DOA). In the Bayesian approach, the underlying DOA is assumed random and its a posteriori probability density function (PDF) is approximated by a discrete probability mass function. A Bayesian beamformer then balances a set of beamformers according to the associated weights. To obtain a close approximation of the a posteriori PDF, the number of samples must be sufficiently large, incurring a significant computational burden. In this paper, we exploit the structure of a uniform linear array (ULA) to show that samples of the a posteriori PDF can be computed efficiently using the fast Fourier transform (FFT). This leads to a fast algorithm for the Bayesian beamformer, which operates in O(MlogM + N2) operations where M is the number of samples and N is the number of sensors.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)0780379977
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
Country/TerritoryUnited States
CitySt. Louis


  • Array signal processing
  • Bayesian methods
  • Computational efficiency
  • Direction of arrival estimation
  • Fast Fourier transforms
  • Frequency estimation
  • Probability density function
  • Radar
  • Sonar
  • Vectors

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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